Triple
T1659374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cross of Valour (Poland) |
E35869
|
entity |
| Predicate | maximumAwardsToSamePerson |
P30989
|
FINISHED |
| Object | 4 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 4 | Statement: [Cross of Valour (Poland), maximumAwardsToSamePerson, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumAwardsToSamePerson Context triple: [Cross of Valour (Poland), maximumAwardsToSamePerson, 4]
-
A.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given year.
-
B.
mostAwardsHolder
Indicates that the subject is the entity that holds the highest number of awards within a given group or context.
-
C.
hasMultipleAwardsIndicatedBy
Indicates that an entity is recognized as having received multiple awards, as evidenced or signaled by a specified source or indicator.
-
D.
numberOfAwards
Indicates the total count of awards that have been received by an entity.
-
E.
maximumNominationsPerFilm
Indicates the highest number of nominations that any single film is allowed to receive.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a88606aa808190aa0b421b4271f220 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf3359ce48190803b322db8ad6027 |
completed | March 6, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69a907cff53c8190b424f088478d3e2c |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a99a4c3810819089d2dd0e23c8e46b |
completed | March 5, 2026, 2:59 p.m. |
Created at: March 4, 2026, 7:29 p.m.